深度学习 python 脚本实现 keras mninst 数字识别 预测端 code

import numpy
import skimage.io
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.models import load_model

#if the picture is bigger than 28*28 will get below error
#ValueError: cannot reshape array of size 775440 into shape (1,28,28,1)

image = 'D:\\sthself\\ml\\reshape7.jpg'

img2 = skimage.io.imread(image,as_grey=True)
skimage.io.imshow(img2)
plt.show()

#img3 is a matrix
img3 = numpy.reshape(img2,(1,28,28,1)).astype('float32')

print(img3)


# rebuild the model  ,do we need to add the layer ?  AttributeError: 'Sequential' object has no attribute 'load_model'

#If you stored the complete model, not only the weights, in the HDF5 file, then it is as simple as
#from keras.models import load_model
#model = load_model('model.h5')
# examples https://stackoverflow.com/questions/35074549/how-to-load-a-model-from-an-hdf5-file-in-keras
modelTrained = load_model('D:\\works\\jetBrians\\PycharmProjects\\tryPicture\\my_model.h5')

# we should get a correct answer is  2
predict = modelTrained.predict(img3, verbose=0)
#list of predicted labels and their probabilities
print(predict[0])
#[ 0.04785086  0.02547075  0.06954221  0.03620625  0.01439319  0.03016909   0.03120618  0.00815302  0.70513636  0.03187207]

# AttributeError: 'Sequential' object has no attribute 'prediect_classes'
result = modelTrained.predict_proba(img3,batch_size=1, verbose=0)
print(result)

print("tensorflow hello word is done")

同事帮忙写的数字

我自己写的数字

程序打印log

重点说明： 我们自己的图片应该是黑底白字 才能被识别

D:\applications\Anaconda3\python.exe D:/works/jetBrians/PycharmProjects/tryPicture/showPicture/ShowPicture.py
Using TensorFlow backend.

2018-03-08 20:43:29.102800: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-03-08 20:43:29.102800: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
[  1.17482814e-05   1.08457927e-03   2.43252050e-02   3.06303948e-02
   1.07244858e-04   1.54377140e-05   1.01265108e-07   9.38272536e-01
   4.20123106e-04   5.13266213e-03]
[[  1.17482814e-05   1.08457927e-03   2.43252050e-02   3.06303948e-02
    1.07244858e-04   1.54377140e-05   1.01265108e-07   9.38272536e-01
    4.20123106e-04   5.13266213e-03]]
tensorflow hello word is done

Process finished with exit code 0

转载于:https://www.cnblogs.com/TendToBigData/p/10501187.html